In many technical processes, pattern recognition acquires increased importance, since an increasing degree of automatization can thereby be achieved. Pattern recognition processes can as a rule be reduced to a time-variant measurement signal derived in a suitable way from the patterns to be recognized. However, in the automatic analysis of this measurement signal the problem arises that these measurement signals are not present in pure form, but rather are overlaid with stationary or non-stationary disturbing signals. In the examination of measurement signals derived from naturally uttered speech, these disturbing portions of the measurement signal are for example caused by background noises, breathing noises, machine noises, or also by the recording medium and the transmission path. Since the measurement signal is never present in pure form, it is particularly important to distinguish between the portions of the measurement signal containing the pattern to be recognized and other portions in which no pattern is present. For the better recognition of the patterns, it is thus particularly important to know exactly when patterns are present in the measurement signal and when no patterns, i.e. signals not resulting from the pattern are present as pause signals in the measurement signal.
A pause detection is for example also important in order to achieve a reduction in the quantity of the transmitted data, for example in speech communication channels and also in satellite transmission, for general distinguishing of useful signal from disturbing signal in signal processing, or else to find the end of an expression in the automatic speech recognition system. A robust pause detector thereby serves for the improvement of the efficiency of speech-controlled systems. This holds in particular for speech recognition systems, since what is concerned there is the comparison of a spoken expression as a pattern with an already-existing version. The problematic of pause determination specifically in automatic speech recognition has been described extensively by Rabiner (L. R. Rabiner and M. Sambur (1995), "An Algorithm for Determining the Endpoints of Isolated Utterances", The Bell system Technical Journal, 54(2), pages 297-315). He has also indicated an algorithm for pause detection. There, for pause detection items of information are taken into account that are calculated directly from the sampled time signal (energy, zero crossing rate, etc.). This procedure is common to all known pause detectors (J. H. Hansen, "Speech Enhancement Employing Boundary Detection and Morphological Based Spectral Constraints", IEEE International Conference On Acoustics, Speech and Signal Processing, pages 901-904, Toronto, ICASSP). As a rule, they use a more or less complicated control apparatus to carry out the classification of the pauses from the calculated features. As an alternative, statistical classifiers have also been used (H. Katterfeldt, "Sprachbestimmung mit Polynom Klassifikatoren", Proceedings Mustererkennung 7, DAGM-Symposium, Erlangen, pages 180-184). Due to this procedure, all these methods can operate only up to a certain disturbance level. The limit depends on the type of disturbance. They can no longer be used with small signal-noise ratios, since as a rule pause detectors are threshold-controlled. However, given very low signal to noise ratios, in environments with disturbances the current decision criteria with thresholds fail. In addition, there are non-stationary disturbances with a character similar to a signal, which can hardly be detected.
Previous approaches to the determination of speech pauses use e.g. a local parameter, i.e. one obtained on the basis of a temporal or, respectively, spectral item of frame information, for the detection of signal or, respectively, non-signal regions (S. Boll, (1979), "Suppression of Acoustic Noise In Speech Using Spectral Subtraction", IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. ASS-27, No. 2, pages 113-120; and B. Widrow et al, (1975), "Adaptive Noise Cancelling: Principles and Applications", Proceedings of the IEEE, 63 (12), pages 1692-1716). Works on this subject published more recently are also primarily based on modifications or expansions of these works. Further procedures for pause recognition in time-variant signals are not known.